TVA analyses

processing, loading

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.2     ✔ tibble    3.3.0
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.4     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(googlesheets4)
library(googledrive)

Attaching package: 'googledrive'

The following objects are masked from 'package:googlesheets4':

    request_generate, request_make

library(ggrepel)
library(lme4)

Loading required package: Matrix

Attaching package: 'Matrix'

The following objects are masked from 'package:tidyr':

    expand, pack, unpack

# posts <- read_sheet("https://docs.google.com/spreadsheets/d/1dDv3Zib0TC9R5GzjACkN6MzsneCPp6w7BPEJ8Kcjp94/edit?gid=2035831840#gid=2035831840")
# 
# folder <- drive_get("https://drive.google.com/drive/folders/1oD6VagXmO0fHFD0GF14P7RbVaXSlAPb_")
# 
# csv_files <- drive_ls(path = folder) %>%
#   filter(grepl("\\.csv$", name))
# 
# # Read all CSVs into a list
# csv_data_list <- map2(
#   .x = csv_files$id,
#   .y = csv_files$name,
#   .f = function(file_id, file_name) {
#     temp_path <- tempfile(fileext = ".csv")
#     drive_download(as_id(file_id), path = temp_path, overwrite = TRUE)
#     read_csv(temp_path)
#   }
# )
# 
# names(csv_data_list) <- csv_files$name
# 
# combined_df <- imap_dfr(
#   .x = csv_data_list,
#   .f = ~ mutate(.x, source_file = .y)
# )
# 
# 
# 
# combined_df <- combined_df |> 
#   mutate(date_of_extract = str_sub(source_file, end = 10)) |> 
#   select(date_of_extract, everything())
# 
# combined_df <- combined_df |> 
#   select(date_of_extract, post_text = Title, everything())
# 
# posts <- posts |> 
#   mutate(date_of_post = str_sub(`Post Date`, end = 10)) |> 
#   fill(Week, .direction = "down") |> 
#   rename(post_text = `Post Text Caption`) |> 
#   select(date_of_post, post_text, Condition)
# 
# combined_df <- combined_df |> 
#   mutate(post_text = str_replace(post_text, '^"', ''))
# 
# combined_df <- combined_df |> 
#   mutate(post_text = str_sub(post_text, end = 30))
# 
# posts <- posts |> 
#   mutate(post_text = str_sub(post_text, end = 30))
# 
# posts

# write_csv(combined_df, "tva_combined.csv")
# write_csv(posts, "tva_posts.csv")

combined_df <- read_csv("tva_combined.csv")

Warning: One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)

Rows: 4645 Columns: 109
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (10): date_of_extract, post_text, Page name, Description, Publish time, ...
dbl (49): Post ID, Page ID, Duration (sec), Is crosspost, Is share, Views, R...
lgl (50): Languages, Custom labels, Funded content status, Data comment, 3-s...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

posts <- read_csv("tva_posts.csv")

Rows: 32 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (2): post_text, Condition
date (1): date_of_post

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# fix a little file issue
combined_df <- combined_df %>% 
  mutate(date_of_extract = ifelse(date_of_extract == "Apr-30-202", "2025-04-30", date_of_extract))

combined_df <- combined_df |> 
  left_join(posts, by = join_by(post_text))

combined_df <- combined_df %>% 
  mutate(Condition = ifelse(is.na(Condition), "BAU", Condition))

combined_df %>% count(Condition)

# A tibble: 3 × 2
  Condition     n
  <chr>     <int>
1 BAU        3842
2 TVA         415
3 Text Only   388

combined_df <- combined_df %>% 
  mutate(date_of_extract_proc = lubridate::ymd(date_of_extract)) %>% 
  mutate(date_of_publication = lubridate::mdy_hm(`Publish time`))

combined_df %>% 
  select(date_of_extract, date_of_extract_proc, date_of_publication, `Publish time`)

# A tibble: 4,645 × 4
   date_of_extract date_of_extract_proc date_of_publication `Publish time`  
   <chr>           <date>               <dttm>              <chr>           
 1 2025-07-31      2025-07-31           2025-07-31 13:01:00 07/31/2025 13:01
 2 2025-07-31      2025-07-31           2025-07-31 10:50:00 07/31/2025 10:50
 3 2025-07-31      2025-07-31           2025-07-31 05:01:00 07/31/2025 05:01
 4 2025-07-31      2025-07-31           2025-07-30 12:25:00 07/30/2025 12:25
 5 2025-07-31      2025-07-31           2025-07-30 07:16:00 07/30/2025 07:16
 6 2025-07-31      2025-07-31           2025-07-29 13:01:00 07/29/2025 13:01
 7 2025-07-31      2025-07-31           2025-07-29 09:02:00 07/29/2025 09:02
 8 2025-07-31      2025-07-31           2025-07-28 13:15:00 07/28/2025 13:15
 9 2025-07-31      2025-07-31           2025-07-28 07:25:00 07/28/2025 07:25
10 2025-07-31      2025-07-31           2025-07-26 07:01:00 07/26/2025 07:01
# ℹ 4,635 more rows

combined_df %>% 
  group_by(post_text) %>% 
  summarize(post_text = first(post_text), 
            date_first_posted = first(date_of_publication),
            date_of_extract_last = last(date_of_extract),
            n = n(),
            condition = first(Condition),
            mean_reactions = mean(Reactions, na.rm = TRUE),
            mean_comments = mean(Comments, na.rm = TRUE),
            mean_shares = mean(Shares, na.rm = TRUE),
            mean_views = mean(Views, na.rm = TRUE),
            mean_reach = mean(Reach, na.rm = TRUE),
            mean_impressions = mean(Impressions, na.rm = TRUE),
            link_clicks = mean(`Link Clicks`, na.rm = TRUE)) %>% 
  write_csv("all-facebook-posts-tva.csv")
            
combined_df %>%
  skimr::skim()

Data summary

Name	Piped data
Number of rows	4645
Number of columns	113
_______________________
Column type frequency:
character	11
Date	2
logical	50
numeric	49
POSIXct	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
date_of_extract	0	1.00	10	10	0	62	0
post_text	44	0.99	29	30	0	208	0
Page name	0	1.00	32	32	0	1	0
Description	4511	0.03	210	827	0	9	0
Publish time	0	1.00	14	16	0	232	0
Caption type	0	1.00	3	8	0	3	0
Permalink	0	1.00	54	109	0	4487	0
Post type	0	1.00	4	6	0	4	0
Date	0	1.00	8	8	0	1	0
source_file	0	1.00	44	86	0	64	0
Condition	0	1.00	3	9	0	3	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date_of_post	3842	0.17	2025-02-25	2025-06-26	2025-04-15	31
date_of_extract_proc	0	1.00	2025-02-25	2025-07-31	2025-05-18	62

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
Languages	4645	0	NaN	:
Custom labels	4645	0	NaN	:
Funded content status	4645	0	NaN	:
Data comment	4645	0	NaN	:
3-second video views by top audience (F, 18-24)	4644	0	1	TRU: 1
3-second video views by country (Australia (AU))	4642	0	1	TRU: 3
3-second video views by country (British Virgin Islands (VG))	4641	0	1	TRU: 4
3-second video views by country (Greece (GR))	4642	0	1	TRU: 3
3-second video views by country (Portugal (PT))	4644	0	1	TRU: 1
3-second video views by country (Malawi (MW))	4644	0	1	TRU: 1
3-second video views by country (Pakistan (PK))	4642	0	1	TRU: 3
3-second video views by country (Philippines (PH))	4644	0	1	TRU: 1
3-second video views by country (Sweden (SE))	4644	0	1	TRU: 1
3-second video views by top audience (M, 18-24)	4645	0	NaN	:
3-second video views by country (United Kingdom (GB))	4645	0	NaN	:
3-second video views by country (Puerto Rico (PR))	4645	0	NaN	:
3-second video views by country (Japan (JP))	4645	0	NaN	:
3-second video views by country (Romania (RO))	4645	0	NaN	:
3-second video views by country (Netherlands (NL))	4645	0	NaN	:
3-second video views by country (Nigeria (NG))	4645	0	NaN	:
3-second video views by country (Turkey (TR))	4645	0	NaN	:
3-second video views by country (Italy (IT))	4645	0	NaN	:
3-second video views by country (New Zealand (NZ))	4645	0	NaN	:
3-second video views by country (Singapore (SG))	4645	0	NaN	:
3-second video views by country (Argentina (AR))	4645	0	NaN	:
3-second video views by country (Ukraine (UA))	4645	0	NaN	:
3-second video views by country (Fiji (FJ))	4645	0	NaN	:
3-second video views by country (Poland (PL))	4645	0	NaN	:
3-second video views by country (Nepal (NP))	4645	0	NaN	:
3-second video views by country (Austria (AT))	4645	0	NaN	:
3-second video views by country (Vietnam (VN))	4645	0	NaN	:
3-second video views by country (Peru (PE))	4645	0	NaN	:
3-second video views by country (Switzerland (CH))	4643	0	1	TRU: 2
3-second video views by country (Israel (IL))	4643	0	1	TRU: 2
3-second video views by country (Ireland (IE))	4643	0	1	TRU: 2
3-second video views by country (Malaysia (MY))	4643	0	1	TRU: 2
3-second video views by country (South Africa (ZA))	4643	0	1	TRU: 2
3-second video views by country (Saudi Arabia (SA))	4643	0	1	TRU: 2
3-second video views by country (Macedonia (MK))	4643	0	1	TRU: 2
3-second video views by country (Costa Rica (CR))	4643	0	1	TRU: 2
3-second video views by country (Guernsey (GG))	4644	0	1	TRU: 1
3-second video views by country (Cyprus (CY))	4643	0	1	TRU: 2
3-second video views by country (American Samoa (AS))	4643	0	1	TRU: 2
3-second video views by country (Jamaica (JM))	4643	0	1	TRU: 2
3-second video views by country (Norway (NO))	4643	0	1	TRU: 2
3-second video views by country (Serbia (RS))	4643	0	1	TRU: 2
3-second video views by country (Brazil (BR))	4643	0	1	TRU: 2
3-second video views by country (United Arab Emirates (AE))	4643	0	1	TRU: 2
3-second video views by country (Georgia (GE))	4644	0	1	TRU: 1
3-second video views by country (Hungary (HU))	4644	0	1	TRU: 1

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Post ID	0	1.00	1.105625e+15	3.170539e+13	1.052957e+15	1.080783e+15	1.103292e+15	1.125767e+15	1.186236e+15	▇▇▇▅▂
Page ID	0	1.00	1.000649e+14	2.471545e+06	1.000649e+14	1.000649e+14	1.000649e+14	1.000649e+14	1.000650e+14	▇▁▁▁▁
Duration (sec)	0	1.00	2.540000e+00	1.964000e+01	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	2.340000e+02	▇▁▁▁▁
Is crosspost	0	1.00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	▁▁▇▁▁
Is share	0	1.00	1.000000e-02	1.000000e-01	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	1.000000e+00	▇▁▁▁▁
Views	32	0.99	7.463280e+03	1.238689e+04	3.570000e+02	1.982000e+03	3.289000e+03	6.752000e+03	9.291100e+04	▇▁▁▁▁
Reach	0	1.00	4.718620e+03	6.875600e+03	2.580000e+02	1.430000e+03	2.291000e+03	4.894000e+03	4.660000e+04	▇▁▁▁▁
Reactions, Comments and Shares	0	1.00	1.312000e+02	2.804700e+02	0.000000e+00	1.200000e+01	3.100000e+01	8.200000e+01	1.720000e+03	▇▁▁▁▁
Reactions	0	1.00	8.910000e+01	1.842200e+02	0.000000e+00	1.000000e+01	2.500000e+01	5.900000e+01	1.054000e+03	▇▁▁▁▁
Comments	0	1.00	9.960000e+00	4.209000e+01	0.000000e+00	0.000000e+00	0.000000e+00	3.000000e+00	4.280000e+02	▇▁▁▁▁
Shares	0	1.00	3.214000e+01	6.963000e+01	0.000000e+00	2.000000e+00	7.000000e+00	2.400000e+01	4.830000e+02	▇▁▁▁▁
Total clicks	0	1.00	2.885400e+02	1.064430e+03	0.000000e+00	1.800000e+01	5.300000e+01	1.430000e+02	1.108700e+04	▇▁▁▁▁
Link Clicks	504	0.89	7.138000e+01	1.911200e+02	1.000000e+00	8.000000e+00	2.000000e+01	5.700000e+01	1.774000e+03	▇▁▁▁▁
Other Clicks	57	0.99	1.995500e+02	8.500700e+02	1.000000e+00	8.000000e+00	2.300000e+01	8.200000e+01	8.765000e+03	▇▁▁▁▁
Matched Audience Targeting Consumption (Photo Click)	654	0.86	3.236000e+01	7.061000e+01	1.000000e+00	3.000000e+00	8.000000e+00	2.500000e+01	5.480000e+02	▇▁▁▁▁
Negative feedback from users: Hide all	4076	0.12	1.430000e+00	5.800000e-01	1.000000e+00	1.000000e+00	1.000000e+00	2.000000e+00	3.000000e+00	▇▁▅▁▁
Negative feedback from users: Hide	4521	0.03	1.310000e+00	4.700000e-01	1.000000e+00	1.000000e+00	1.000000e+00	2.000000e+00	2.000000e+00	▇▁▁▁▃
Seconds viewed	4511	0.03	5.532125e+04	8.121570e+04	1.531430e+03	2.530930e+03	6.538620e+03	1.797391e+05	1.908877e+05	▇▁▁▁▃
Average Seconds viewed	4511	0.03	6.220000e+00	2.880000e+00	3.360000e+00	3.650000e+00	5.580000e+00	1.008000e+01	1.236000e+01	▇▅▁▃▂
Estimated earnings (USD)	4513	0.03	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	▁▁▇▁▁
Ad CPM (USD)	4511	0.03	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	▁▁▇▁▁
Ad impressions	4511	0.03	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	▁▁▇▁▁
Impressions	4604	0.01	4.456320e+03	3.950580e+03	6.880000e+02	1.560000e+03	2.933000e+03	5.754000e+03	1.455600e+04	▇▃▁▁▂
Negative feedback from users	4336	0.07	2.000000e-01	5.300000e-01	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	3.000000e+00	▇▁▁▁▁
Unique negative feedback from users	4336	0.07	2.000000e-01	5.300000e-01	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	3.000000e+00	▇▁▁▁▁
Unique negative feedback from users: Hide all	4608	0.01	1.430000e+00	6.000000e-01	1.000000e+00	1.000000e+00	1.000000e+00	2.000000e+00	3.000000e+00	▇▁▅▁▁
Unique negative feedback from users: Hide	4637	0.00	1.000000e+00	0.000000e+00	1.000000e+00	1.000000e+00	1.000000e+00	1.000000e+00	1.000000e+00	▁▁▇▁▁
3-second video views by top audience (F, 35-44)	4635	0.00	4.164000e+02	7.904400e+02	3.100000e+01	3.200000e+01	4.550000e+01	6.275000e+01	1.921000e+03	▇▁▁▁▂
3-second video views by top audience (F, 45-54)	4635	0.00	4.792000e+02	9.100700e+02	2.000000e+01	2.400000e+01	5.750000e+01	1.037500e+02	2.210000e+03	▇▁▁▁▂
3-second video views by top audience (F, 55-64)	4635	0.00	2.956000e+02	5.650400e+02	1.700000e+01	2.000000e+01	2.950000e+01	4.425000e+01	1.373000e+03	▇▁▁▁▂
3-second video views by top audience (F, 65+)	4635	0.00	2.925000e+02	5.726800e+02	1.200000e+01	1.700000e+01	2.350000e+01	3.100000e+01	1.383000e+03	▇▁▁▁▂
3-second video views by top audience (F, 25-34)	4635	0.00	1.564000e+02	3.028700e+02	9.000000e+00	9.250000e+00	1.250000e+01	2.000000e+01	7.330000e+02	▇▁▁▁▂
3-second video views by top audience (M, 45-54)	4635	0.00	5.790000e+01	1.123300e+02	3.000000e+00	3.250000e+00	5.000000e+00	7.000000e+00	2.720000e+02	▇▁▁▁▂
3-second video views by top audience (M, 35-44)	4635	0.00	6.160000e+01	1.151600e+02	2.000000e+00	6.000000e+00	8.000000e+00	1.200000e+01	2.810000e+02	▇▁▁▁▂
3-second video views by top audience (M, 65+)	4635	0.00	5.300000e+01	1.036000e+02	2.000000e+00	3.000000e+00	3.500000e+00	8.500000e+00	2.520000e+02	▇▁▁▁▂
3-second video views by top audience (M, 55-64)	4636	0.00	5.156000e+01	9.750000e+01	1.000000e+00	2.000000e+00	3.000000e+00	4.000000e+00	2.260000e+02	▇▁▁▁▂
3-second video views by top audience (M, 25-34)	4640	0.00	3.880000e+01	4.904000e+01	1.000000e+00	4.000000e+00	4.000000e+00	9.200000e+01	9.300000e+01	▇▁▁▁▅
3-second video views by country (United States (US))	4635	0.00	1.856800e+03	3.570690e+03	9.900000e+01	1.070000e+02	1.875000e+02	2.790000e+02	8.658000e+03	▇▁▁▁▂
3-second video views by country (Canada (CA))	4636	0.00	1.911000e+01	3.141000e+01	2.000000e+00	3.000000e+00	4.000000e+00	4.000000e+00	7.500000e+01	▇▁▁▁▂
3-second video views by country (Mexico (MX))	4639	0.00	2.000000e+00	1.550000e+00	1.000000e+00	1.000000e+00	1.000000e+00	3.250000e+00	4.000000e+00	▇▁▁▁▃
3-second video views by country (South Korea (KR))	4638	0.00	1.570000e+00	9.800000e-01	1.000000e+00	1.000000e+00	1.000000e+00	2.000000e+00	3.000000e+00	▇▁▁▁▃
3-second video views by country (Taiwan (TW))	4640	0.00	1.000000e+00	0.000000e+00	1.000000e+00	1.000000e+00	1.000000e+00	1.000000e+00	1.000000e+00	▁▁▇▁▁
3-second video views by country (Spain (ES))	4641	0.00	2.000000e+00	1.150000e+00	1.000000e+00	1.000000e+00	2.000000e+00	3.000000e+00	3.000000e+00	▇▁▁▁▇
3-second video views by country (Germany (DE))	4641	0.00	5.250000e+00	4.920000e+00	1.000000e+00	1.000000e+00	5.000000e+00	9.250000e+00	1.000000e+01	▇▁▁▁▇
3-second video views by country (Kenya (KE))	4643	0.00	1.000000e+00	0.000000e+00	1.000000e+00	1.000000e+00	1.000000e+00	1.000000e+00	1.000000e+00	▁▁▇▁▁
3-second video views by country (Thailand (TH))	4641	0.00	2.500000e+00	5.800000e-01	2.000000e+00	2.000000e+00	2.500000e+00	3.000000e+00	3.000000e+00	▇▁▁▁▇
3-second video views by country (Lebanon (LB))	4643	0.00	1.000000e+00	0.000000e+00	1.000000e+00	1.000000e+00	1.000000e+00	1.000000e+00	1.000000e+00	▁▁▇▁▁
3-second video views by country (India (IN))	4641	0.00	2.500000e+00	1.730000e+00	1.000000e+00	1.000000e+00	2.500000e+00	4.000000e+00	4.000000e+00	▇▁▁▁▇
3-second video views by country (Montenegro (ME))	4643	0.00	1.000000e+00	0.000000e+00	1.000000e+00	1.000000e+00	1.000000e+00	1.000000e+00	1.000000e+00	▁▁▇▁▁

Variable type: POSIXct

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date_of_publication	0	1	2025-02-17 08:02:00	2025-07-31 13:01:00	2025-04-17 07:24:00	225

only_tva_and_text <- combined_df %>% 
  filter(Condition == "TVA" | Condition == "Text Only")

models

mean_reactions_model <- lmer(Reactions ~ Condition + (1 | post_text), data = only_tva_and_text)
sjPlot::tab_model(mean_reactions_model)

	Reactions
Predictors	Estimates	CI	p
(Intercept)	19.58	1.45 – 37.71	0.034
Condition [TVA]	34.86	9.63 – 60.10	0.007
Random Effects
σ2	39.19
τ00 post_text	1277.87
ICC	0.97
N post_text	31
Observations	803
Marginal R2 / Conditional R2	0.187 / 0.976

mean_comments_model <- lmer(Comments ~ Condition + (1 | post_text), data = only_tva_and_text)
sjPlot::tab_model(mean_comments_model)

	Comments
Predictors	Estimates	CI	p
(Intercept)	0.26	-0.32 – 0.84	0.379
Condition [TVA]	0.96	0.15 – 1.77	0.020
Random Effects
σ2	0.03
τ00 post_text	1.30
ICC	0.97
N post_text	31
Observations	803
Marginal R2 / Conditional R2	0.147 / 0.978

mean_shares_model <- lmer(Shares ~ Condition + (1 | post_text), data = only_tva_and_text)
sjPlot::tab_model(mean_shares_model)

	Shares
Predictors	Estimates	CI	p
(Intercept)	5.85	-0.72 – 12.41	0.081
Condition [TVA]	15.38	6.24 – 24.52	0.001
Random Effects
σ2	2.85
τ00 post_text	167.66
ICC	0.98
N post_text	31
Observations	803
Marginal R2 / Conditional R2	0.257 / 0.988

mean_views_model <- lmer(Views ~ Condition + (1 | post_text), data = only_tva_and_text)
sjPlot::tab_model(mean_views_model)

	Views
Predictors	Estimates	CI	p
(Intercept)	3253.10	1765.31 – 4740.89	<0.001
Condition [TVA]	2489.44	418.54 – 4560.35	0.019
Random Effects
σ2	333529.65
τ00 post_text	8601627.53
ICC	0.96
N post_text	31
Observations	803
Marginal R2 / Conditional R2	0.148 / 0.968

mean_reach_model <- lmer(Reach ~ Condition + (1 | post_text), data = only_tva_and_text)
sjPlot::tab_model(mean_reach_model)

	Reach
Predictors	Estimates	CI	p
(Intercept)	2374.11	1272.10 – 3476.12	<0.001
Condition [TVA]	1830.68	296.76 – 3364.60	0.019
Random Effects
σ2	169618.52
τ00 post_text	4719804.04
ICC	0.97
N post_text	31
Observations	803
Marginal R2 / Conditional R2	0.146 / 0.970

mean_link_clicks_model <- lmer(`Link Clicks` ~ Condition + (1 | post_text), data = only_tva_and_text)
sjPlot::tab_model(mean_link_clicks_model)

	Link Clicks
Predictors	Estimates	CI	p
(Intercept)	54.87	33.28 – 76.46	<0.001
Condition [TVA]	-16.20	-46.25 – 13.86	0.290
Random Effects
σ2	33.03
τ00 post_text	1813.51
ICC	0.98
N post_text	31
Observations	802
Marginal R2 / Conditional R2	0.034 / 0.983

mean_total_clicks_model <- lmer(`Total clicks` ~ Condition + (1 | post_text), data = only_tva_and_text)
sjPlot::tab_model(mean_total_clicks_model)

	Total clicks
Predictors	Estimates	CI	p
(Intercept)	104.92	41.73 – 168.11	0.001
Condition [TVA]	35.60	-52.35 – 123.55	0.427
Random Effects
σ2	226.07
τ00 post_text	15531.82
ICC	0.99
N post_text	31
Observations	803
Marginal R2 / Conditional R2	0.020 / 0.986

summary stats

combined_df |> 
  select(date_of_extract, date_of_post, Condition, post_text, 
         Reactions, Shares, Comments, `Reactions, Comments and Shares`,
         Views, 
         # Impressions, mostly missing - 91% missing, others complete
         Reach, 
         `Total clicks`, `Other Clicks`, `Link Clicks`) |> # link clicks, 9% missing
  gather(key, val, -date_of_extract, -date_of_post, -Condition, -post_text) |> 
  # filter(!is.na(date_of_post)) |> 
  mutate(date_of_extract = lubridate::ymd(date_of_extract)) |> 
  mutate(date_of_post = lubridate::ymd(date_of_post)) |> 
  mutate(day_diff = date_of_extract - date_of_post) |> 
  # filter(day_diff > 0) %>% 
  group_by(Condition, key) %>% 
  summarize(mean_val = median(val, na.rm = TRUE),) %>% 
  spread(Condition, mean_val)

`summarise()` has grouped output by 'Condition'. You can override using the
`.groups` argument.

# A tibble: 9 × 4
  key                               BAU `Text Only`   TVA
  <chr>                           <dbl>       <dbl> <dbl>
1 Comments                          0            0      1
2 Link Clicks                      16           55     24
3 Other Clicks                     19           50     30
4 Reach                          2055         2379   3136
5 Reactions                        24.5         16     35
6 Reactions, Comments and Shares   31           20     50
7 Shares                            6            5     13
8 Total clicks                     43          108     87
9 Views                          2958.        3294.  4600

plots

p <- combined_df |> 
  select(date_of_extract, date_of_post, Condition, post_text, 
    Reactions, Shares, Comments, `Reactions, Comments and Shares`,
    Views, 
    # Impressions, mostly missing - 91% missing, others complete
    Reach, 
    `Total clicks`, `Other Clicks`, `Link Clicks`) |> # link clicks, 9% missing
  gather(key, val, -date_of_extract, -date_of_post, -Condition, -post_text) |> 
  # filter(!is.na(date_of_post)) |> 
  mutate(date_of_extract = lubridate::ymd(date_of_extract)) |> 
  mutate(date_of_post = lubridate::ymd(date_of_post)) |> 
  mutate(day_diff = date_of_extract - date_of_post) |> 
  # filter(day_diff > 0) %>% 
  ggplot(aes(y = val, x = day_diff, group = post_text, color = Condition)) +
  geom_point() +
  geom_line() +
  theme_minimal() +
  scale_color_brewer(palette = 1, type = "qual") +
  facet_wrap(~key, scales = "free_y") +
  scale_x_continuous(limits = c(0, 40), breaks = seq(0, 30, by = 5))

p

Warning: Removed 37578 rows containing missing values or values outside the scale range
(`geom_point()`).

Warning: Removed 34911 rows containing missing values or values outside the scale range
(`geom_line()`).

p %>% plotly::ggplotly()